Enhancing wind power forecasting accuracy with hybrid deep learning and teaching-learning-based optimization
Forecasting wind power generation is crucial for ensuring grid security and the competitiveness of the power market. This paper presents an innovative approach that combines deep learning (DL) with Teaching-Learning-Based Optimization (TLBO) to predict wind power output accurately. Using a real data...
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| Format: | Article |
| Language: | English |
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Elsevier B.V.
2024
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| Online Access: | http://umpir.ump.edu.my/id/eprint/42920/ http://umpir.ump.edu.my/id/eprint/42920/1/Enhancing%20wind%20power%20forecasting%20accuracy%20with%20hybrid.pdf |
| _version_ | 1848826735558131712 |
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| author | Mohd Herwan, Sulaiman Zuriani, Mustaffa |
| author_facet | Mohd Herwan, Sulaiman Zuriani, Mustaffa |
| author_sort | Mohd Herwan, Sulaiman |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Forecasting wind power generation is crucial for ensuring grid security and the competitiveness of the power market. This paper presents an innovative approach that combines deep learning (DL) with Teaching-Learning-Based Optimization (TLBO) to predict wind power output accurately. Using a real dataset spanning diverse weather conditions and turbine specifications collected between January 2018 and March 2020, the study employs 18 features as inputs, including Ambient Temperature, Wind Direction, and Wind Speed, with real power output in kW as the target variable. Metaheuristic algorithms including Particle Swarm Optimization (PSO), Barnacles Mating Optimizer (BMO), Biogeography-Based Optimization (BBO), and Firefly Algorithm (FA) are comprehensively compared for model optimization. TLBO-DL consistently provides forecasts that closely align with actual wind power values across instances, substantiated by its low RMSE of 98.7601, indicating effective minimization of errors in wind power forecasting. Comparative analysis with other algorithms reveals that TLBO-DL outperforms PSO-DL (RMSE: 102.6627), BMO-DL (RMSE: 132.4839), BBO-DL (RMSE: 103.8517), and FA-DL (RMSE: 104.7282) in terms of overall forecasting accuracy. The variations in the performance of other algorithms across instances highlight the robustness and effectiveness of TLBO-DL in achieving accurate wind power forecasts. Overall, TLBO-DL emerges as a reliable and superior algorithm for wind power forecasting, consistently providing accurate forecasts across a range of instances. |
| first_indexed | 2025-11-15T03:49:33Z |
| format | Article |
| id | ump-42920 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:49:33Z |
| publishDate | 2024 |
| publisher | Elsevier B.V. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-429202025-03-03T07:41:50Z http://umpir.ump.edu.my/id/eprint/42920/ Enhancing wind power forecasting accuracy with hybrid deep learning and teaching-learning-based optimization Mohd Herwan, Sulaiman Zuriani, Mustaffa QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering Forecasting wind power generation is crucial for ensuring grid security and the competitiveness of the power market. This paper presents an innovative approach that combines deep learning (DL) with Teaching-Learning-Based Optimization (TLBO) to predict wind power output accurately. Using a real dataset spanning diverse weather conditions and turbine specifications collected between January 2018 and March 2020, the study employs 18 features as inputs, including Ambient Temperature, Wind Direction, and Wind Speed, with real power output in kW as the target variable. Metaheuristic algorithms including Particle Swarm Optimization (PSO), Barnacles Mating Optimizer (BMO), Biogeography-Based Optimization (BBO), and Firefly Algorithm (FA) are comprehensively compared for model optimization. TLBO-DL consistently provides forecasts that closely align with actual wind power values across instances, substantiated by its low RMSE of 98.7601, indicating effective minimization of errors in wind power forecasting. Comparative analysis with other algorithms reveals that TLBO-DL outperforms PSO-DL (RMSE: 102.6627), BMO-DL (RMSE: 132.4839), BBO-DL (RMSE: 103.8517), and FA-DL (RMSE: 104.7282) in terms of overall forecasting accuracy. The variations in the performance of other algorithms across instances highlight the robustness and effectiveness of TLBO-DL in achieving accurate wind power forecasts. Overall, TLBO-DL emerges as a reliable and superior algorithm for wind power forecasting, consistently providing accurate forecasts across a range of instances. Elsevier B.V. 2024-12 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/42920/1/Enhancing%20wind%20power%20forecasting%20accuracy%20with%20hybrid.pdf Mohd Herwan, Sulaiman and Zuriani, Mustaffa (2024) Enhancing wind power forecasting accuracy with hybrid deep learning and teaching-learning-based optimization. Cleaner Energy Systems, 9 (100139). pp. 1-12. ISSN 2772-7831. (Published) https://doi.org/10.1016/j.cles.2024.100139 https://doi.org/10.1016/j.cles.2024.100139 |
| spellingShingle | QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering Mohd Herwan, Sulaiman Zuriani, Mustaffa Enhancing wind power forecasting accuracy with hybrid deep learning and teaching-learning-based optimization |
| title | Enhancing wind power forecasting accuracy with hybrid deep learning and teaching-learning-based optimization |
| title_full | Enhancing wind power forecasting accuracy with hybrid deep learning and teaching-learning-based optimization |
| title_fullStr | Enhancing wind power forecasting accuracy with hybrid deep learning and teaching-learning-based optimization |
| title_full_unstemmed | Enhancing wind power forecasting accuracy with hybrid deep learning and teaching-learning-based optimization |
| title_short | Enhancing wind power forecasting accuracy with hybrid deep learning and teaching-learning-based optimization |
| title_sort | enhancing wind power forecasting accuracy with hybrid deep learning and teaching-learning-based optimization |
| topic | QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/42920/ http://umpir.ump.edu.my/id/eprint/42920/ http://umpir.ump.edu.my/id/eprint/42920/ http://umpir.ump.edu.my/id/eprint/42920/1/Enhancing%20wind%20power%20forecasting%20accuracy%20with%20hybrid.pdf |